Submitted:
18 June 2026
Posted:
22 June 2026
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Abstract
The article examines the transition from traditional to digital experiences by analyzing the relationships between attitudes toward digital and non-digital environments in four domains: navigation, work, public transportation, and shopping. In four studies, participants’ attitudes were examined toward a parallel digital experience (a navigation app, remote work, the use of digital means in public transportation, and online shopping) compared to the traditional experience. In three contexts (navigation, remote work, and online shopping), a negative correlation between attitudes was found, indicating that the technology is perceived as a substitute for the traditional environment. In contrast, in the context of public transportation, a positive relationship was found, as digital means are perceived as a complementary component to the physical experience. The findings are interpreted using the exemplar approach to memory, according to which digital and traditional experiences are stored in separate or overlapping sets of exemplars. The article proposes an extension to classical technology acceptance models and highlights practical implications for designing implementation processes that emphasize experiential continuity between the traditional and digital environments.
Keywords:
digital environment
; digital experience
; navigation app
; remote work
; online shopping
In recent decades, rapid technological advancement has brought about a fundamental transformation in the structure of modern life, and its impact is evident across all areas of human activity — from work and learning to communication and the performance of everyday tasks. Digital technologies have become an integral part of daily life, enabling routine activities such as online shopping, real-time navigation, remote learning, and hybrid work. However, alongside the many opportunities offered by digital advancement, there are groups within the population—particularly older adults—who face difficulties in adopting and adapting to new technologies. Understanding the factors that influence individuals’ willingness to adopt technological innovations is a key condition for the optimal integration of the entire population into an evolving technological reality.
This study seeks to examine the factors contributing to difficulties in adopting new technologies, with a focus on differences between attitudes toward the use of technological versus traditional means. Over the years, various theoretical models have been proposed to understand technology adoption processes, among them the Technology Acceptance Model (TAM), which emphasizes the contribution of perceived usefulness and perceived ease of use to users’ attitudes and actual behavior (Badda, 2025). Alongside this approach, other researchers have proposed frameworks that incorporate social, cultural, and emotional dimensions—such as norms, incentives, sense of self-efficacy, and trust (Gunasinghe et al., 2019; Martin, 2022).
One of the areas in which technology acceptance has been extensively examined is online learning. It was found that the adoption of innovative teaching technologies by academic staff is a key factor in the success of implementation, and that attitudes toward distance learning are influenced by perceived usefulness (performance expectancy), perceived ease of use (effort expectancy), perceived risk, perceived behavioral control, social influence, and facilitating conditions (Gunasinghe et al., 2019). Despite the growing popularity of distance learning, not all learners express interest in or willingness to participate in it. Its success depends on the learners’ level of engagement, comfort, enjoyment, and perceived effectiveness (Gao, 2019; Panigrahi, Srivastava, & Sharma, 2018). Therefore, blended learning environments — which combine online instruction with face-to-face teaching — may offer a balanced and effective response to diverse learning needs.
Another domain with a broad impact is the use of social media, which has fundamentally transformed patterns of communication, information consumption, and interactions between individuals and organizations (Duong, 2020). Social media has become a central communication tool in marketing and business contexts as well, due to its ability to enable two-way interaction between brands and users (Aichner et al., 2021).
In recent years, there has been growing interest in understanding the ways in which artificial intelligence-based technologies are adopted. Contemporary models in this field broaden the theoretical focus by examining not only users’ willingness to adopt AI technologies, but also their level of resistance to them—particularly in situations where such technologies may replace human workers (Kelly, Kaye, & Oviedo-Trespalacios, 2023). Variables such as behavioral intention, willingness to use, and actual behavior have been identified as key predictors of AI technology acceptance, and understanding them may assist in making strategic decisions in this field.
Another area in which the issue of technology adoption is evident is remote work. Although such arrangements offer many advantages—reducing travel, easing traffic congestion, and saving office space—not all employees are quick to adopt them (Laumer & Maier, 2021). Studies indicate that concerns about social isolation and professional loneliness may make it difficult for employees to adopt working from home, whereas perceptions of convenience, usefulness, and the ability to maintain professional connections enhance their willingness to adopt it. In addition, the lack of a suitable home workspace has been identified as another significant factor influencing the decision not to work remotely (Laumer & Maier, 2021).
Although many studies have examined the factors influencing users’ willingness to adopt technologies in various domains — such as online learning, social media, remote work, and artificial intelligence — existing knowledge still lacks a clear understanding of the transition process itself between non-technological and technological environments. That is, how does the transition from traditional to digital conduct occur, and what are the personal and systemic mechanisms that facilitate or hinder it. A deep understanding of this process is essential both theoretically—for constructing comprehensive models of technology adoption—and practically—for developing optimal processes for integrating new technologies into various areas of life. Therefore, the present study seeks to examine in depth the process of transitioning to a technological (digital) environment, with a focus on the cognitive, emotional, and behavioral aspects accompanying this change.
The experience in the digital world can also be understood in theoretical terms. When it is unconscious, it can be described as a cluster of memory exemplars, whereas a conscious experience is not a collection of exemplars but is instead based on the memory of a single exemplar (Logan, 2002). Alternatively, the unconscious experience can be described in probabilistic terms, such that memory simultaneously contains multiple representations, whereas the conscious experience is confined to a single memory that is no longer described probabilistically. This approach is based on a theory that views reality itself as a kind of virtual reality (Fields, Hoffman, Prakash, & Singh, 2018), according to which every phenomenon in nature — including memories and neurons — can be described probabilistically.
According to this theory, and of particular importance to our discussion, just as in virtual reality the characteristics of an object are determined only at the moment of observation or measurement, so too in reality — the parameters are defined only at the moment of measurement, and prior to that they must be described in probabilistic terms. Additionally, just as in virtual reality there are minimal values for each parameter; in reality as well it is not possible to measure values below a certain threshold. And finally, just as a change in one parameter within virtual reality may influence another parameter in a different location, so too in reality—a change in one variable may affect another variable in a place that is perceived as distant.
Accordingly, memory can be viewed as comprising a collection of exemplars accumulated through exposure to a particular environment—digital or non-digital—each of which may also be described in probabilistic terms. If there is a high overlap between the two memory repositories (digital and non-digital), a positive correlation is expected between attitudes toward experiences in both environments. In contrast, when the two repositories are distinct and differ from one another, a negative correlation is expected between attitudes toward the digital experience and attitudes toward the non-digital experience.
Therefore, the aim of the study is to examine the relationships between attitudes toward the use of digital means and attitudes toward the use of non-digital means, in order to determine whether the two environments are perceived as similar or distinct experiences. To this end, four studies were conducted:
In the first study, the relationship between reported attitudes toward the navigation experience when using a navigation app and attitudes toward navigation without the app was examined; in the second study, the relationship between attitudes toward remote work and attitudes toward working in an office; in the third study, the relationship between attitudes toward the use of digital tools and attitudes toward the use of non-digital means in public transportation; and in the fourth study, the relationship between attitudes toward the online shopping experience and attitudes toward the in-store shopping experience.
Study 1
Method
Participants
The study included 54 drivers, of whom 45 were women. Three participants were aged 20–25, twenty were aged 26–30, fourteen were aged 31–40, nine were aged 41–55, and seven were over 55 years old. Regarding driving experience, five participants had held a driver’s license for less than five years, twelve had 5–10 years of experience, and thirty-seven had held a license for more than ten years.
Instruments
The study included two questionnaires:
Demographic Questionnaire – This questionnaire included items related to gender, age, and driving experience.
Attitudes Toward Navigation Questionnaire – This questionnaire examined attitudes toward driving with and without the use of a dedicated navigation app. Participants were asked to rate their agreement with various statements on a five-point Likert scale (1 = to a very small extent, 5 = to a very large extent). The questionnaire items were developed based on existing instruments measuring attitudes toward computer and internet use (Badda, 2025).
Examples of items include:
“Using a navigation app helps me navigate efficiently,” “Driving with a navigation app shortens travel time ,”“The navigation app serves as a significant aid while driving,” “I enjoy driving with a navigation app,” “When I use a navigation app, I feel calmer during the drive. “I am satisfied with the way I interact with the navigation app while driving,” “Using a navigation app makes me feel safer on the road,” “Driving with a navigation app makes the drive more pleasant for me,” “I am satisfied with the drive when I use a navigation app,” “I am satisfied with the drive when I do not use a navigation app,” “During a drive without a navigation app, I feel safe”, “When driving without a navigation app, I am satisfied with my interaction with the road,” “I manage to arrive on time without using a navigation app,” “I enjoy the drive without using a navigation app.”
Procedure
Participants were recruited using a convenience sampling method. Participation in the study was entirely voluntary and anonymous. The questionnaires were administered online and distributed to participants via email. In the introduction to the questionnaire, it was emphasized that the data would be used for research purposes only and that no personal use would be made of the information collected.
Results
The relationships between participants’ attitudes toward the driving experience when using a navigation application and their attitudes toward the driving experience without using such an application were examined. Specifically, correlations were analyzed between the mean attitudes toward driving with a navigation app, the mean attitudes toward driving without a navigation app, age, gender, and driving experience (in years). The reliability of the scale assessing attitudes toward driving with a navigation app was high (α = 0.889), and the reliability of the scale assessing attitudes toward driving without a navigation app was also high (α = 0.933). Table 1 presents the correlations among the examined variables.
A regression analysis was conducted to examine the relationships between the various measures and attitudes toward driving with the assistance of dedicated navigation software (the dependent variable). The regression model is significant and explains 30.1% of the variance (adjusted R square=.301), F (4, 53) =6.714, p<.05. Table 2 presents the values of the regression analysis.
The analysis results indicate a negative relationship between attitudes toward driving with the assistance of dedicated navigation software and attitudes toward driving without using such software. In addition, no significant relationship was found between age, gender, or driving experience and attitudes toward driving with the assistance of dedicated navigation software.
In addition, it was found that drivers prefer to drive using a dedicated navigation application. The mean score on the questionnaire assessing driving with the assistance of a navigation application was 3.75 (SD = 0.61), whereas the mean score on the questionnaire assessing driving without the use of such an application was 2.35 (SD = 0.83). This difference was found to be statistically significant, t(53) = 7.96, p < .001.
Study 2
Method
Participants
Sixty-three participants (32 women) took part in the study, aged 18 to 54 (M = 27.35, SD = 8.53). Twenty of the participants completed the questionnaire in Hebrew and 43 in English. Eight participants had children: one participant had one child, three participants had two children, three participants had three children, and one participant had four children.
Participants indicated the percentage of their job performed remotely (with a score of 1 representing 10% of a full-time position). Nineteen participants did not work from home at all (0%), and seven reported working fully remotely (100%). The average percentage of remote work was 3.05 (SD = 3.44).
Of all participants, 38 were Israeli, and the rest were from various countries, including the United States, Germany, Italy, the United Kingdom, Mexico, Japan, India, Ukraine, Greece, Norway, Finland, and Iraq.
Instruments
The study was based on two main questionnaires:
Demographic Questionnaire – This questionnaire included items referring to gender, age, number of children, and the percentage of the participant’s current work performed from home.
Attitudes toward working from home and from the office questionnaire – This questionnaire contained statements describing various attitudes toward remote work and in-office work. Participants were asked to rate their level of agreement with each statement on a five-point Likert scale (1 = to a very small extent, 5 = to a very large extent). The questionnaire items were adapted from existing instruments previously examined in the context of attitudes toward computer and Internet use (Badda, 2025).
Examples of items included in the questionnaire: “Remote work is effective,” “Remote work contributes to my career development,” “I am efficient when working remotely,” “My time management is effective when I work remotely,” and “I am satisfied with my daily communication with my manager and team when working remotely.”
Similar items were presented regarding in-office work, for example: “in-office work is effective,” “in-office work develops my personal skills,” and “I am efficient when working in the office.”
Procedure:
The employees were recruited for the study using a convenience sampling method. As in the previous study, participation was voluntary. The questionnaires were administered online and sent to the participants via email. The introduction to the questionnaire explicitly stated that participation in the study was anonymous and based on free will, and that the data collected would be used for research purposes only.
Results
The relationships between participants’ attitudes toward the remote work experience and their attitudes toward the in-office work experience were examined. Specifically, correlations were tested among the mean scores of attitudes toward remote work experience, mean scores of attitudes toward in-office work experience, age, gender, number of children, and the percentage of remote work in the current position. The reliability of the scale measuring participants’ attitudes toward the remote work experience was found to be high (Cronbach’s Alpha = 0.920), and the reliability of the scale measuring attitudes toward the in-office work experience was also high (α = 0.878). Table 3 presents the correlations among all variables examined.
A regression analysis was conducted to examine the relationships between the various measures and attitudes toward remote work (the dependent variable). The regression model is significant and explains 14.6% of the variance (adjusted R square=.146), F (6, 51) =2.457, p<.05. Table 4 presents the values of the regression analysis.
The analysis results indicate a negative relationship between attitudes toward in-office work and attitudes toward remote work. No significant relationship was found between age, gender, or number of children and attitudes toward remote work. However, the percentage of remote work was found to be positively related to attitudes toward remote work.
Table 4.
Correlations between attitudes toward remote work (dependent variable) and attitudes toward in-office work, percentage of remote work, number of children, age, gender, and group (English, Hebrew).
Table 4.
Correlations between attitudes toward remote work (dependent variable) and attitudes toward in-office work, percentage of remote work, number of children, age, gender, and group (English, Hebrew).
| Variables | B | Std. Error | Beta | T | Sig. |
|---|---|---|---|---|---|
| Attitudes toward in-office work | -.405 | .203 | -.267 | -1.995 | .052 |
| Work-from-home percentage | .099 | .033 | .390 | 2.989 | .005 |
| Age | .016 | .021 | .151 | .750 | .457 |
| Number of children | -.252 | .195 | -.260 | -1.291 | .203 |
| Gender | -.084 | .236 | -.049 | -.357 | .722 |
| Group | .106 | .273 | .052 | .389 | .699 |
In addition, it was found that employees tended to prefer in-office work. The mean score for the questionnaire assessing attitudes toward remote work was 3.34 (SD = 0.84), whereas the mean score for the questionnaire assessing attitudes toward in-office work was 3.88 (SD = 0.60). A t-test analysis indicated that the difference between the means was statistically significant, t(62)=−3.15, p<.001.
Study 3
Method
Participants
The study included 63 participants, of whom 32 were women. Participants’ ages ranged from 18 to 67 years (M = 46.98, SD = 14.54). The number of children reported by participants ranged from zero to ten (M = 3.54, SD = 2.74).
Instruments
The study was based on two questionnaires.
The first questionnaire was a demographic questionnaire, which included items relating to the participants’ gender, age, and number of children.
The second questionnaire examined attitudes toward the use of digital means and attitudes toward the physical experience of bus travel. This questionnaire included statements describing different feelings and perceptions regarding public transportation. Participants were asked to rate each statement according to their level of agreement, using a five-point Likert scale (1 = to a very small extent, 5 = to a very large extent).
The questionnaire items were developed based on existing questionnaires that assess attitudes toward computer and Internet use (Badda, 2025). Examples of statements included in the questionnaire are: “Public transportation rides are comfortable and the seats are convenient,” “The space and general atmosphere in public transport are pleasant,” “The ride is calm and enjoyable,” “There is no sense of crowding on the bus,” “The service adheres to the planned schedule,” “I am satisfied with the level of cleanliness at the stations and on the bus itself,” “I am satisfied with the technological–digital support in public transportation,” “Using digital technology during the ride enhances my overall experience,” “I enjoy using navigation applications during the ride,” “Technology helps me arrive on time for the bus,” “Technology in public transportation is easy to use,” and “It is easy for me to pay on the bus using digital tools.”
Procedure
Participants were recruited for the study using a convenience sampling method. Participation was entirely voluntary. The questionnaires were administered online and distributed to respondents via email. The introduction to the questionnaire explicitly stated that participation in the study was anonymous and that the data provided by participants would be used solely for research purposes. All participants reported that they use public transportation at least several times a year.
Results
The relationships between participants’ attitudes toward the physical experience of traveling by public transportation and their attitudes toward the digital experience associated with using digital means during the trip were examined. Specifically, correlations were tested between the average scores of attitudes toward the physical experience, average scores of attitudes toward the digital experience, age, gender, and number of children. The reliability of the scale assessing participants’ attitudes toward the physical experience was found to be moderate (Cronbach’s Alpha = 0.676), whereas the reliability of the scale assessing attitudes toward the digital experience during the trip was high (Cronbach’s Alpha = 0.921). Table 5 presents the correlations between the examined variables.
A regression analysis was conducted to examine the relationships between various measures and attitudes toward the use of digital tools (the dependent variable). The regression model is significant and explains 46.3% of the variance (adjusted R square=.463), F (4, 52) =12.200, p<.001. Table 6 presents the values of the regression analysis.
The findings indicate that there is a correlation between attitudes toward physical travel and attitudes toward the use of digital means. Additionally, age and number of children were found to be related to attitudes toward the use of digital means, with the relationships between age and number of children and attitudes toward digital use being negative.
Study 4
Method
Participants
Sixty participants took part in the study, of which 55 were women. Participants’ ages ranged from 18 to 63 years (mean age = 35.45, SD = 12.37).
Instruments
The study was based on two questionnaires.
The first, a demographic questionnaire, included items regarding the participants’ gender and age.
The second was an attitudes questionnaire that examined participants’ views toward the use of digital means when shopping online and their experience of in-store shopping. This questionnaire included statements describing different aspects of shopping, and participants were asked to rate their level of agreement with each statement on a five-point Likert scale (1 = to a very small extent, 5 = to a very large extent.(
The questionnaire items were developed based on existing tools used in studies examining attitudes toward computer and Internet use (Badda, 2025). Examples of the items used included statements such as: “Shopping in-store improves my mood,” “I feel a sense of security when shopping in-store,” “I enjoy interacting with salespeople when shopping in-store,” “It is pleasant to make purchases in-store,” “The store atmosphere is pleasant,” “The salespeople are courteous,” “The variety of products in stores is wide,” “I am satisfied with the cleanliness of the store,” “I am satisfied with the quality of products received when purchasing online,” “Online shopping saves me time,” “I enjoy the online shopping process,” “Internet purchases bring me enjoyment,” “I am satisfied with the variety available on shopping websites,” “I am satisfied with the customer service on the site,” and “I am satisfied with the quality of the products delivered.”
Procedure
Participants in this study were also recruited using a convenience sampling method. Participation in the study was voluntary, and the questionnaires were distributed online and sent to participants via email. The introduction to the questionnaire explicitly stated that participation was free and anonymous, and that the data collected would be used for research purposes only. All participants reported making online purchases at least several times a year.
Results
The relationships between participants’ attitudes toward shopping in a physical store and their attitudes toward online shopping were examined. Specifically, the correlations between the mean attitudes toward online shopping, the mean attitudes toward the in-store shopping experience, and age were tested. The reliability of the scale assessing participants’ attitudes toward the in-store shopping experience was moderate (α = 0.832), whereas the reliability of the scale measuring attitudes toward the online shopping experience was high (α = 0.936). Table 7 presents the correlations among the examined variables.
A regression analysis was conducted to examine the relationships between the various measures and attitudes toward purchasing through digital means (the dependent variable). The regression model is significant and explains 10.6% of the variance (adjusted R square=.106), F (3, 59) =3.33, p<.05. Table 8 presents the values of the regression analysis.
The findings of the analysis indicate a negative relationship between attitudes toward online shopping and attitudes toward in-store shopping. In addition, no significant relationship was found between age or gender and attitudes toward online shopping.
In addition, it was found that buyers tend to prefer online shopping over in-store shopping. The mean response to the questionnaire assessing attitudes toward online shopping was 3.90 (SD = 0.63), whereas the mean response to the questionnaire assessing attitudes toward in-store shopping was 3.64 (SD = 0.61). This difference was found to be statistically significant, t(59)=−1.93, p<.05.
Discussion
The findings from the four studies indicate a fairly consistent pattern of relationships between attitudes toward digital environments and attitudes toward non-digital environments, but also reveal some intriguing differences. In Study 1, a negative correlation was found between attitudes toward using navigation software and attitudes toward navigating without the software. Similarly, in Study 2, a negative correlation emerged between attitudes toward remote work and attitudes toward working in the office. In Study 4, a negative correlation was found between attitudes toward online shopping and attitudes toward shopping in physical stores. In contrast, Study 3 revealed a positive correlation between attitudes toward the experience of traveling by public transportation and attitudes toward using digital tools during the trip.
This pattern indicates that, in most of the contexts examined, the digital environment and the non-digital environment are not experienced as variations of the same phenomenon, but rather as distinct experiences. When technology is perceived as a substitute for a traditional activity (for example, navigating with an app instead of without one, working from home instead of in an office, or shopping online instead of in a store), attitudes toward the two environments tend to be oppositional: a preference for one is associated with a less favorable attitude toward the other. In contrast, in the context of public transportation, digital means do not replace the physical journey but rather supplement and integrate with it (for instance, in navigation, payment, or real-time information); therefore, the digital experience serves as an addition and continuation of the underlying experience, and the associations between attitudes are positive.
This difference is consistent with the theoretical proposition that memory operates as a repository of exemplars, accumulated through repeated experiences—digital or non-digital. According to the exemplar approach, every encounter with a technology or with a traditional medium adds a new exemplar to the relevant memory store. When the two memory stores share similar characteristics—namely, when the digital and non-digital experiences occur within a similar physical context—there may be a high degree of overlap, manifested in positive correlations between attitudes toward the two environments. When the stores are distinct—for example, when the digital experience replaces the traditional setting and is perceived as a separate environment—positive attitudes toward one are expected to coincide with less favorable attitudes toward the other.
Accordingly, it is possible to propose a distinction between two types of transition: a transition in which digital technology replaces a traditional activity (for example, remote work instead of in-office work, online shopping instead of shopping in a physical store, or navigation using an app instead of navigating without one). In this case, separate sets of examples often develop for each environment, and the relationship between attitudes is expected to be negative.
A transition in which digital technology accompanies an existing activity rather than replacing it (for example, using digital tools while riding a bus). In this case, the digital examples are more closely related and similar to the physical experience examples, and therefore the relationships between the attitudes may be positive.
This framework expands on classical technology acceptance models, such as TAM, which focus on perceived usefulness and ease of use as key factors shaping attitudes and intentions to use technology. These models typically examine the acceptance of the technology itself but do not address how experiential memory is organized in relation to the environment that the technology replaces or complements. The present study suggests that, in addition to variables such as perceived usefulness, ease of use, social norms, and self-efficacy, it is also important to consider the structure of experiential memory—namely, whether digital and traditional experiences converge into the same set of exemplars or remain separated into two distinct clusters of examples.
From a practical perspective, the findings suggest that the implementation of new technologies in environments where they replace existing practices may encounter resistance stemming not only from considerations of usefulness and ease of use, but also from the organization of experiential memory around two distinct environments. In such cases, it is advisable to design the implementation processes to emphasize continuity and overlap between the traditional and digital experiences—for example, by creating shared touchpoints, preserving familiar elements, and allowing time for the gradual accumulation of positive examples during the digital experience. Conversely, in environments where the technology serves as a complement (such as public transportation), the existing experiential overlap can be leveraged to strengthen the positive associations between digital use and the physical experience.
The present study therefore highlights the importance of examining the transition between environments—rather than focusing solely on attitudes toward each environment separately—and suggests that for some users, the shift from a traditional to a digital environment is not perceived as an extension of the same experience but as an entirely different world. Accordingly, effective technological change processes should consider not only which technologies are adopted, but also how they are encoded in memory: whether they are registered as a continuation of what existed before or as a new experience that requires separate adaptation.
The present study indicates that when transitioning from traditional environments to digital ones, participants’ attitudes are organized not only around whether the technology is perceived as useful and easy to use, but also around how digital and non-digital experiences are encoded in memory—as similar or distinct experiential categories.
Summary of the Main Findings
Across all four studies, relationships were examined between attitudes toward digital experiences and attitudes toward corresponding non-digital experiences (navigation, work, public transportation, and shopping). In three contexts (navigation, remote work, and online shopping), a negative correlation was found between these attitudes—that is, a preference for the digital environment was associated with less positive attitudes toward the traditional environment, and vice versa. In contrast, in the context of public transportation—where digital tools do not replace the physical trip but rather complement it—a positive relationship was found between attitudes toward the physical experience and attitudes toward the use of digital tools during travel.
Limitations and Weaknesses of the Study
There are several limitations that should be considered when interpreting the findings:
The samples in all studies were convenience samples, which do not necessarily represent the general population, thus limiting the external validity of the results.
Most of the examined contexts were based on self-reported Likert-type questionnaires, which do not necessarily reflect actual behavior and are susceptible to social desirability and selective memory biases.
The correlational design (and cross-sample regressions) does not allow for causal inference regarding the exact direction of influence between digital attitudes and traditional attitudes.
In some measures, particularly in the measure of physical experience in public transportation, the reliability of the instrument was only moderate, which may affect the stability of the observed relationships.
Furthermore, although the discussion draws on theories of memory as exemplars and probabilistic representations, no direct measures of memory processes or of the probabilistic representation of experience clusters were collected; instead, attitudes were used as an indirect indicator of memory structure.
Suggestions for Future Research
Building on the current study, several key directions can be proposed to deepen the understanding of the transition processes between digital and non-digital environments:
Integrating performance and behavioral measures (e.g., actual navigation app usage data, frequency of remote work, volume of online purchases) alongside attitude questionnaires, to examine the link between the structure of experiential memory and real behavior.
Testing multi-level models that explicitly combine classical TAM variables (perceived usefulness, ease of use, social norms) with cognitive measures of memory organization (e.g., perceived similarity between digital and traditional experiences, or indices of overlap between “exemplar clusters”).
Expanding the research to additional contexts in which digital technologies may function either as substitutes or as complementary components (e.g., online versus in-class teaching, telemedicine versus in-person visits), to explore whether positive/negative association patterns of attitudes are replicated.
Employing longitudinal-experimental designs that allow for tracking the accumulation of experiential examples over time and for testing how gradual exposure to positive or negative digital experiences affects attitudes toward traditional environments and the overlap between memory repositories.
Examining different populations, such as older adults or employees in diverse fields, to identify individual differences in the tendency to organize digital experiences as substitutes or supplements to traditional experiences, and accordingly develop tailored implementation strategies.
In this way, future studies could refine the theoretical understanding of transitions between experiential environments and contribute to an informed design of technological change processes—ones that take into account not only usefulness and ease of use, but also the ways in which digital innovation becomes “registered” within users’ memory systems.
Data availability: The datasets generated during and/or analysed during the current study are available from the corresponding author (Amotz Perlman) on reasonable request.
No funds, grants, or other support was received
The authors have no relevant financial or non-financial interests to disclose
The authors have no competing interests to declare that are relevant to the content of this article.
There is an approval from a research ethics committee of Ashkelon Academic College (2025).
Results from this study have been published as preprints but not elsewhere.
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Table 1.
The relationships between the different continuous variables measured in the study.
| Variables | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1. Attitudes toward navigation software | ||||
| 2. Attitudes toward driving without a navigation app | -.584** | |||
| 3. Driving experience | -.162 | .088 | ||
| 4. Age | .040 | -.192 | .483** | |
| **. Correlation is significant at the 0.01 level (2-tailed). | ||||
| *. Correlation is significant at the 0.05 level (2-tailed). | ||||
Table 2.
Associations between attitudes toward driving with navigation software assistance (dependent variable) and attitudes toward driving without dedicated navigation software, driving experience, age, and gender.
Table 2.
Associations between attitudes toward driving with navigation software assistance (dependent variable) and attitudes toward driving without dedicated navigation software, driving experience, age, and gender.
| Variables | B | Std. Error | Beta | T | Sig. |
|---|---|---|---|---|---|
| Attitudes toward driving without a navigation app | -.426 | .089 | -.579 | -4.776 | <.001 |
| Driving experience | -.094 | .125 | -.101 | -.749 | .457 |
| Age | -.012 | .074 | -.022 | -.159 | .875 |
| Gender | .003 | .192 | .002 | .016 | .987 |
Table 3.
The relationships between the various continuous variables measured in the study.
| Variables | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1. Attitudes toward remote work | |||||
| 2. Attitudes toward in-office work | -.293* | ||||
| 3. Number of children | -.071 | -.101 | |||
| 4. Work-from-home percentage | .403** | -.004 | .013 | ||
| 5. Age | .009 | -.025 | .779**. | .044 | |
| **. Correlation is significant at the 0.01 level (2-tailed). | |||||
| *. Correlation is significant at the 0.05 level (2-tailed). | |||||
Table 5.
The relationships between the various continuous variables measured in the study.
| Variables | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1. Attitudes toward digital use | ||||
| 2. Attitudes toward physical travel | .328* | |||
| 3. Number of children | -.621** | -.138 | ||
| 4. Age | -.635** | -.273 | .715** | |
| **. Correlation is significant at the 0.01 level (2-tailed). | ||||
| *. Correlation is significant at the 0.05 level (2-tailed). | ||||
Table 6.
The relationship between attitudes toward digital use (dependent variable) and attitudes toward physical commuting, number of children, age, and gender.
Table 6.
The relationship between attitudes toward digital use (dependent variable) and attitudes toward physical commuting, number of children, age, and gender.
| Variables | B | Std. Error | Beta | T | Sig. |
|---|---|---|---|---|---|
| Attitudes toward physical travel | .373 | .188 | .209 | 1.982 | .053 |
| Age | -.020 | .010 | -.311 | -2.055 | .045 |
| Number of children | -.127 | .051 | -.365 | -2.498 | .016 |
| Gender | .150 | .201 | .078 | .745 | .460 |
Table 7.
The relationships between the various continuous variables measured in the study.
| Variables | 1 | 2 | 3 |
|---|---|---|---|
| 1. Attitudes toward online shopping | |||
| 2. Attitudes toward in-store shopping | -.385** | ||
| 3. Age | -.097 | .328* | . |
| **. Correlation is significant at the 0.01 level (2-tailed). | |||
| *. Correlation is significant at the 0.05 level (2-tailed). |
Table 8.
The relationships between attitudes toward online shopping (dependent variable) and attitudes toward in-store shopping, age, and gender.
Table 8.
The relationships between attitudes toward online shopping (dependent variable) and attitudes toward in-store shopping, age, and gender.
| Variables | B | Std. Error | Beta | T | Sig. |
|---|---|---|---|---|---|
| Attitudes toward in-store shopping | -.410 | .135 | -.398 | -3.051 | .003 |
| Age | .002 | .007 | .039 | .300 | .765 |
| Gender | .107 | .257 | .052 | .418 | .678 |
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